Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+# Load the fine-tuned EE LLM
+model_name = "STEM-AI-mtl/phi-2-electrical-engineering"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+gen_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=256)
+
+# Define function to generate answer
+def generate_answer(question):
+    prompt = f"Answer this electronics engineering question:\n{question}\nAnswer:"
+    response = gen_pipeline(prompt, do_sample=True, temperature=0.7)[0]["generated_text"]
+    answer = response.split("Answer:")[-1].strip()
+    return answer
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## 🤖 Ask Me Electronics Engineering Questions")
+    question = gr.Textbox(label="Your Question", placeholder="e.g. What is a BJT?")
+    output = gr.Textbox(label="AI Answer", lines=4)
+    button = gr.Button("Generate Answer")
+    button.click(generate_answer, inputs=question, outputs=output)
+
+if __name__ == "__main__":
+    demo.launch()